Real-time ML enables new classes of systems that can quickly adapt to environments, such as real-time fraud detection, intrusion detection, and real-time personalized-recommendation systems. Feature engineering is the most challenging aspect of for real-time ML, and online feature pipelines are the key component of any solution.
In this webinar, we will introduce the challenges of building online feature pipelines, including integration with a feature store (to avoid training/serving skew), online model serving infrastructure, and real-time feature computation, with stream processing and on-demand features. We will present these challenges in the context of the Hopsworks platform, and show how it reduces the time required to put real-time ML in production.
In this webinar we will explain the core concepts of great expectations and how we made them available on Hopsworks to be used within your feature pipelines. Users are able to define expectation suites or reuse their existing ones.
The Feature Store is the essential part of AI infrastructure that helps organisations bring modern enterprise data to analytical and operational ML systems. It is the simplest most powerful way to get your models to production. From anywhere, to anywhere.From months, to minutes.